The null hypothesis assumes no difference between groups .Testing Data normality revealed non non-normal distribution so I opted for the Mann Whitney , but testing for Levene Homogeneity of Variance and Box plots showed unequal distribution . does that mean I should accept the null ? or do I have to proceed with other test ?
(1) "Accepting" the null is misleading wording anyway (despite being used all over the place) as not rejecting a null hypothesis never means that it is true anyway.
(2) To some extent this depends on what you are interested in. The Wilcoxon MW test will compare rank sums between two groups. The distribution of the test statistic is computed assuming that both distributions are the same, but null hypotheses are never perfectly fulfilled anyway. So you are well "within your rights" to use the test and interpret the result as follows:
Reject: Rank sums are significantly more different than to be expected if distributions were equal, so there is evidence that one distribution tends to produce larger values than the other.
Not reject: The rank sum difference between the groups is not clearly different from what would be expected under equal distributions, so there is no evidence that one distribution produces systematically larger values than the other. This does not mean that distributions are the same, neither does it provide evidence in favour of this. In fact we can see that distributions are not the same, however the test result shows that despite an obvious difference in variation, there may not be any clear difference regarding which distribution generally tends to produce larger values.
So if you're interested in making a statement about whether one group generally produces larger values than the other (in terms of rank sums), and you are not interested in differences in variation, I'd say you can still use the test, although it may be somewhat risky as the way I am putting things here seems nonstandard in the literature and some may find it dodgy.
PS: This generalises to the Kruskal Wallis test for more than two groups.
PPS: As rightly pointed out in a comment, in case of outliers in the data Wilcoxon WM is usually preferable to a t-test based on normality, because the former gives the outliers less influence, same Kruskal-Wallis.